Stock Analysts Use Instinct When Forecasting Hard-to-Value Firms

When stock analysts aren’t sure how to assess the earnings of a hard to value firm, they often just predict those earnings will follow the general trend of the market, according to new research from the University of Iowa.

“If there’s too much uncertainty and they don’t have a lot of information to go with, analysts look at overall market sentiment to guide their forecasts,” says Paul Hribar, professor of accounting in the UI Tippie College of Business.

Hribar said this bow to investor sentiment is a form of bias that often influences how analysts forecast the earnings of the firms they cover. Sentiment, Hribar says, is driven as much by emotion as by analysis and can reflect errors in investors’ expectations about future payoffs, leading to mispriced stocks and a market that doesn’t reflect underlying fundamentals.

Yet, Hribar’s study appears to show that analysts’ forecasts are heavily influenced by investor sentiment when firms don’t have a lot of data to analyze.

Hribar and his study co-author, John McInnis of the University of Texas at Austin and a UI PhD alumnus, examined analysts’ earnings per share forecasts and long-term earnings growth forecasts for every month from August 1983 to December 2006. From a final sample of more than 646,000 monthly observations, they looked at how accurate those forecasts were one year after they were published.

One thing they quickly found was that analysts are exceedingly optimistic, over-estimating actual earnings in every month in the sample period. In cases where analysts were optimistic, actual earnings did not measure up to forecast earnings. In those cases where analysts were pessimistic, they weren’t pessimistic enough because the firms lost more money than the analysts predicted.

The study also found that some firms proved especially troublesome for analysts to forecast, particularly when it come to small firms, young firms, unprofitable firms, stocks with high volatility, and stocks with no dividends. In those cases, the analysts simply don’t have enough data to make a sufficiently informed forecast, so they appear to apply existing sentiment to help generate their forecasts.

“When sentiment is high, earnings forecasts become more optimistic for those firms, and when sentiment is low, the forecasts follow suit,” says Hribar. The pattern follows in both monthly forecasts and long-term quarterly and annual forecasts.

Hribar said the research took into account the fact that the firms could not be arbitraged as a way to explain the difference between forecast and actual earnings, but found that was not responsible. The researchers also considered the idea that forecasters were intentionally overstating earnings estimates to drive a bull market, but their analysis found that was not the case either, so that any over-optimism is unintentional.

Instead, he says the analysts seem to apply an subconscious bias and assume the stock will perform as well or as poorly as they expect the rest of the market to perform, reflecting investor sentiment.

“Analysts rarely say they don’t know, but in a lot of these cases, it would be better for them to say they don’t know,” he says. The reason is because investors rely on these forecasts to make investment decisions, and if those decisions in the end are being made only on overall investor sentiment, then those stocks are mispriced and the market is not an accurate reflection of economic performance.